A Comparative Study on the Effect of Welding Parameters of Austenitic Stainless Steels Using Artificial Neural Network and Taguchi Approaches with ANOVA Analysis

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Kurt H. I., Oduncuoglu M., Yilmaz N. F., Ergul E., Asmatulu R.

METALS, vol.8, no.5, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 8 Issue: 5
  • Publication Date: 2018
  • Doi Number: 10.3390/met8050326
  • Journal Name: METALS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: stud welding, mechanical properties, ANN, Taguchi, ANOVA, MECHANICAL-PROPERTIES, TENSILE-STRENGTH, BEHAVIOR
  • Yıldız Technical University Affiliated: No


In order to investigate the structure of welds, austenitic stainless steel ( SS) studs with a diameter of 6 mm were welded to austenitic SS plates with a thickness of 5 mm using an arc stud welding (ASW) method. The effects of the welding current, welding time, and tip volume of the stud on the microstructure and ultimate tensile strength (UTS) of the welded samples were investigated in detail. The formation of delta-ferrites was detected in the weld zone because of the higher heat generated during the welding process. Higher welding current and time adversely affected the stud and significantly reduced the UTS of the samples. The UTS of the joints was also estimated using artificial neural network (ANN) and Taguchi approaches. The mathematical formulations for these two approaches were given in explicit form. Experimental results showed that the neural network results are more consistent with experimental results than those of the Taguchi method. Overall, it can be concluded that in order to achieve good welding joints and high strength values, ASW parameters should be investigated properly to determine the optimum conditions for each metal.